Update app.py

app.py

@@ -109,11 +109,11 @@ def load_and_prepare_model(model_id):
     model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
     dtype = model_dtypes.get(model_id, torch.bfloat16)  # Default to float32 if not found
     #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
-    vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
+    #vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
     #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
     #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
     #vaeX = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
-    #vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
     #vaeX = AutoencoderKL.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
     #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
   #  vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
@@ -220,7 +220,7 @@ def uploadNote():
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a custom before cuda \n")
-        f.write(f"Model VAE: SG161222/sdxl_vae \n")
+        f.write(f"Model VAE: juggernaut to bfloat before cuda \n")
         f.write(f"Model UNET: default SG161222/RealVisXL_V5.0 \n")
         f.write(f"Model HiDiffusion OFF \n")
     upload_to_ftp(filename)